Optimization techniques for spectrum handoff in cognitive radio networks using cluster based cooperative spectrum sensing

Devi, Monisha; Umamaheswari, K.

doi:10.1007/s11276-021-02549-7

Cited by 16 publications

(6 citation statements)

References 33 publications

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“…The proposed Component-Specific CSS Model (CSCSSM) was implemented in MATLAB. The CSCSSM was compared to the Priority-Based Two-Stage Detection Model (PBTSDM) [ 24 ], Spectrum Binary Particle Swarm Optimization and Queuing Model (SpecBPSO-QM) [ 28 ], Optimum Relay Selection and Accurate Cooperative Spectrum Sensing for Hybrid Cognitive Radio Networks (ORS-ACSS) [ 29 ], and Adapt-Then-Combine (ATC) method [ 35 ]. The CSCSSM and the compared methods were analyzed in terms of Network MSD (dB) by varying the time ( i ).…”

Section: Resultsmentioning

confidence: 99%

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Spectrum Sensing Based on Hybrid Spectrum Handoff in Cognitive Radio Networks

Vaduganathan,

Neware,

Falkowski-Gilski

et al. 2023

Entropy

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The rapid advancement of wireless communication combined with insufficient spectrum exploitation opens the door for the expansion of novel wireless services. Cognitive radio network (CRN) technology makes it possible to periodically access the open spectrum bands, which in turn improves the effectiveness of CRNs. Spectrum sensing (SS), which allows unauthorized users to locate open spectrum bands, plays a fundamental part in CRNs. A precise approximation of the power spectrum is essential to accomplish this. On the assumption that each SU’s parameter vector contains some globally and partially shared parameters, spectrum sensing is viewed as a parameter estimation issue. Distributed and cooperative spectrum sensing (CSS) is a key component of this concept. This work introduces a new component-specific cooperative spectrum sensing model (CSCSSM) in CRNs considering the amplitude and phase components of the input signal including Component Specific Adaptive Estimation (CSAE) for mean squared deviation (MSD) formulation. The proposed concept ensures minimum information loss compared to the traditional methods that consider error calculation among the direct signal vectors. The experimental results and performance analysis prove the robustness and efficiency of the proposed work over the traditional methods.

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Section: Resultsmentioning

confidence: 99%

“…In 2021, Devi and Umamaheswari [ 28 ] included the use of the M/G/1 queuing model and the Spectrum Binary Particle Swarm Optimization (Spec BPSO) algorithm for the prediction of an efficient spectrum handoff method. Cluster-based CSS (CBCSS) was employed to increase SU effectiveness and decrease channel congestion.…”

Section: Literature Reviewmentioning

confidence: 99%

Spectrum Sensing Based on Hybrid Spectrum Handoff in Cognitive Radio Networks

Vaduganathan,

Neware,

Falkowski-Gilski

et al. 2023

Entropy

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“…[13][14][15][16] However, the problem associated with the proactive SH is the obsoletion of the TCS due to the dynamic channel condition that led to the call-dropping and results in poor handoff performance. 12,17,[18][19][20][21] In a pure reactive handoff strategy, this possibility is less as the TCS is prepared by wideband sensing once the handoff triggering event occurs. However, the major drawback of the reactive SH strategy is additional handoff delay that makes it improper for real-time communication.…”

Section: Literature Surveymentioning

confidence: 99%

“…Proactive handoff considerably reduces the handoff delay and the total service time, because instant wideband sensing is not required for this handoff 13–16 . However, the problem associated with the proactive SH is the obsoletion of the TCS due to the dynamic channel condition that led to the call‐dropping and results in poor handoff performance 12,17,18–21 . In a pure reactive handoff strategy, this possibility is less as the TCS is prepared by wideband sensing once the handoff triggering event occurs.…”

Section: Introductionmentioning

confidence: 99%

A novel spectrum handoff switching decision scheme for improved performance of secondary users in cognitive radio network

Dey

Misra

2022

Int J Communication

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Spectrum handoff (SH) in the cognitive radio network (CRN) is considered as a key challenging area to enhance the performance of secondary users (SUs) in CRN. If the primary user is detected, the SU may pause and stay on the same channel or may perform SH to another idle channel. An accurate and precise handoff decision improves the overall throughput and quality of experience of end-users. In this paper, we introduce a new SH algorithm and continuous short-sensing strategy to improve the overall throughput of SUs. In addition, we have derived the minimum length of the target channel sequence based on network-specific parameters like desired call dropping probability. Further, an optimum channel search time is obtained to minimize the handoff delay. The simulation result shows that the proposed scheme improves the overall throughput of CRN, and the mean opinion score of different video applications increases by 10%, 4.6%, and 1% for rapid motion, gentle walk, and slight motion types of video applications. In the case of VoIP applications, the maximum simultaneous call is improved by 2 times in the case of G.711, 1.

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“…In [17], to detect the malicious user, K Nearest Neighbor K-NN, has been used, where without any knowledge of the malicious user, detection can be made. In [18] & [19], advanced K-NN and Dempster-Shafer (D-S) were implemented to detect malicious SUs in the PU boundary coverage.…”

Section: Literature Surveymentioning

confidence: 99%

Identifying Malicious Secondary User Presence Within Primary User Range in Cognitive Radio Networks

Brinda¹,

Bhuvaneshwari

2021

Wireless Pers Commun

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Cognitive Radio (CR) is a widely used wireless radio communication to utilize the available spectrum space of licensed users (Primary Users) efficiently. The primary Users (PU) unused spectrum space for a particular period is allocated to the Secondary Users (SU). The most alarming issue in this assignment is security, in which the secondary users disguise as primary users and misuses the allotted spectrum. To address this issue, in this paper we have proposed the Boundary detection method that uses the estimated location of each SU, which is obtained using the Recurrent Neural Network algorithm, to determine the boundary of PU coverage. Then Malicious User Detection by Ordering (MUDO) methodology is proposed, in which all secondary users are ordered, and based on the orders the SUs are paired with corresponding PUs. The SUs with the least orders are discarded as they might be malicious users. The proposed methodology possesses higher detection speed and precise detection thereby enhancing the performance of Cognitive Radio (CR).

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Optimization techniques for spectrum handoff in cognitive radio networks using cluster based cooperative spectrum sensing

Cited by 16 publications

References 33 publications

Spectrum Sensing Based on Hybrid Spectrum Handoff in Cognitive Radio Networks

Spectrum Sensing Based on Hybrid Spectrum Handoff in Cognitive Radio Networks

A novel spectrum handoff switching decision scheme for improved performance of secondary users in cognitive radio network

Identifying Malicious Secondary User Presence Within Primary User Range in Cognitive Radio Networks

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